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Estimating base temperatures in econometric models that include degree days

Author

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  • Woods, James
  • Fuller, Cody

Abstract

Heating and cooling degree days are in common use in conditional demand models, billing analysis, and large-scale energy forecasts. The implications of either choosing an ex-ante base temperature, or scanning over the base temperatures, as suggested in Fels (1986) and recommended by some evaluation protocols, are infrequently considered. These procedures result in biased estimates of weather-driven loads because of correlated errors-in-variables, and impart a downward bias to the variance of those estimates by a factor of two. A non-linear estimation procedure that corrects for these biases and an ex-ante correction factor for evaluating prior evaluations are offered.

Suggested Citation

  • Woods, James & Fuller, Cody, 2014. "Estimating base temperatures in econometric models that include degree days," Energy Economics, Elsevier, vol. 45(C), pages 166-171.
    • Handle: RePEc:eee:eneeco:v:45:y:2014:i:c:p:166-171
    DOI: 10.1016/j.eneco.2014.06.006
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    References listed on IDEAS

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    1. Lach, Saul, 1993. "Decomposition of Variables and Correlated Measurement Errors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 34(3), pages 715-725, August.
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    Cited by:

    1. Huang, Luling & Nock, Destenie & Cong, Shuchen & Qiu, Yueming (Lucy), 2023. "Inequalities across cooling and heating in households: Energy equity gaps," Energy Policy, Elsevier, vol. 182(C).
    2. Wang, Yaoping & Bielicki, Jeffrey M., 2018. "Acclimation and the response of hourly electricity loads to meteorological variables," Energy, Elsevier, vol. 142(C), pages 473-485.
    3. Kang, Jieyi & Reiner, David M., 2022. "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Energy Economics, Elsevier, vol. 111(C).
    4. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    5. Ozhegov, Evgeniy & Popova, Evgeniya, 2017. "Demand for electricity and weather conditions: Nonparametric analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 46, pages 55-73.
    6. Hu, Wenxuan & Scholz, Yvonne & Yeligeti, Madhura & Deng, Ying & Jochem, Patrick, 2024. "Future electricity demand for Europe: Unraveling the dynamics of the Temperature Response Function," Applied Energy, Elsevier, vol. 368(C).

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    More about this item

    Keywords

    Nonlinear models; Evaluation; Errors-in-variables; Weather statistics; Billing analysis; Demand forecasting;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L95 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Gas Utilities; Pipelines; Water Utilities
    • D19 - Microeconomics - - Household Behavior - - - Other

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